Increasing Power in Phase III Oncology Trials With Multivariable Regression: An Empirical Assessment of 535 Primary End Point Analyses.

Autor:	Sherry AD; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX., Passy AH; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX., McCaw ZR; Insitro, South San Francisco, CA.; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC., Abi Jaoude J; Department of Radiation Oncology, Stanford University, Stanford, CA., Lin TA; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD., Kouzy R; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX., Miller AM; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX., Kupferman GS; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX., Beck EJ; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX., Msaouel P; Department of Genitourinary Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.; Department of Translational Molecular Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX., Ludmir EB; Department of Gastrointestinal Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2024 Sep; Vol. 8, pp. e2400102.
DOI:	10.1200/CCI.24.00102
Abstrakt:	Purpose: A previous study demonstrated that power against the (unobserved) true effect for the primary end point (PEP) of most phase III oncology trials is low, suggesting an increased risk of false-negative findings in the field of late-phase oncology. Fitting models with prognostic covariates is a potential solution to improve power; however, the extent to which trials leverage this approach, and its impact on trial interpretation at scale, is unknown. To that end, we hypothesized that phase III trials using multivariable PEP analyses are more likely to demonstrate superiority versus trials with univariable analyses. Methods: PEP analyses were reviewed from trials registered on ClinicalTrials.gov. Adjusted odds ratios (aORs) were calculated by logistic regressions. Results: Of the 535 trials enrolling 454,824 patients, 69% (n = 368) used a multivariable PEP analysis. Trials with multivariable PEP analyses were more likely to demonstrate PEP superiority (57% [209 of 368] v 42% [70 of 167]; aOR, 1.78 [95% CI, 1.18 to 2.72]; P = .007). Among trials with a multivariable PEP model, 16 conditioned on covariates and 352 stratified by covariates. However, 108 (35%) of 312 trials with stratified analyses lost power by categorizing a continuous variable, which was especially common among immunotherapy trials (aOR, 2.45 [95% CI, 1.23 to 4.92]; P = .01). Conclusion: Trials increasing power by fitting multivariable models were more likely to demonstrate PEP superiority than trials with unadjusted analysis. Underutilization of conditioning models and empirical power loss associated with covariate categorization required by stratification were identified as barriers to power gains. These findings underscore the opportunity to increase power in phase III trials with conventional methodology and improve patient access to effective novel therapies.
Databáze:	MEDLINE
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